Antenna array processing in wireless communications and radar systems

Antenna arrays have shown great promise for improving performance in wireless communication and radar systems. In this dissertation, new approaches and design algorithms are suggested for producing antenna array signal processing schemes that achieve improved performance. First, a modified space-time adaptive processing (STAP) scheme is proposed for airborne radar signal detection. A simple model for ground clutter is used to aid the estimation of the covariance matrix in a nonhomogeneous environment. The resulting algorithm provides good performance without much training data. Next, distributed detection of a known signal in t-distributed noise is studied for cases where the observations are correlated from sensor to sensor. A Gauss-Seidel algorithm which attempts to minimize the Bayes risk is used to obtain the optimum decision rules. It is shown that the optimum sensor decision rules can be predicted in a simple way based on the problem's parameters. Finally the exploitation of transmit diversity and receive diversity in wireless communications is considered by using space-time codes. Systematic code search procedures with low complexity are proposed for space-time codes employing multiple trellis coded modulation (MTCM). MTCM is shown to be necessary for achieving maximum transmit diversity gain when trellis codes with parallel paths are used. By properly labeling the trellis branches, it is demonstrated that the overall coding gain can be made larger than that achieved by conventional space-time codes with the same throughput and the same number of states. A new family of space-time codes is proposed using a serial concatenation structure. An interleaver is embedded between the inner code and the outer code. Analysis shows that by using this structure, both diversity gain and interleaving gain can be achieved for system performance improvement.